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This study is focused on the assessment of the potential of Sentinel-2 satellite images and the Random Forest classifier for mapping forest cover and forest types in northwest Gabon. The main goal was to investigate the impact of various spectral bands collected by the Sentinel-2 satellite, normalized difference vegetation index (NDVI) and digital elevation model (DEM), and their combination on the accuracy of the classification of forest cover and forest type. Within the study area, five classes of forest type were delineated: semi-evergreen moist forest, lowland forest, freshwater swamp forest, mangroves, and disturbed natural forest. The classification was performed using the Random Forest (RF) classifier. The overall accuracy for the forest cover ranged between 92.6% and 98.5%, whereas for forest type, the accuracy was 83.4 to 97.4%. The highest accuracy for forest cover and forest type classifications were obtained using a combination of spectral bands at spatial resolutions of 10 m and 20 m and DEM. In both cases, the use of the NDVI did not increase the classification accuracy. The DEM was shown to be the most important variable in distinguishing the forest type. Among the Sentinel-2 spectral bands, the red-edge followed by the SWIR contributed the most to the accuracy of the forest type classification. Additionally, the Random Forest model for forest cover classification was successfully transferred from one master image to other images. In contrast, the transferability of the forest type model was more complex, because of the heterogeneity of the forest type and environmental conditions across the study area.

Terrestrial ecosystems currently function as a net carbon (C) sink for atmospheric C dioxide (CO2), but whether this C sink can persist with global climate change is still uncertain. Such uncertainty largely comes from C turnover time in an ecosystem, which is a critical parameter for modelling C cycle and evaluating C sink potential. Our current understanding of how long C can be stored in soils and vegetation and what controls spatial variations in C turnover time on a large scale is still very limited. We used data on C stocks and C influx from 2,753 plots in vegetation and 1,087 plots in soils and investigated the spatial patterns as well controlling factors of C turnover times across forest ecosystems in eastern China. Our results showed a clear latitudinal pattern of C turnover times, with the shortest turnover times in the low‐latitude zones and the longest turnover times in the high‐latitude zones. Mean annual temperature and mean annual precipitation were the most important controlling factors on soil C turnover times, while forest age accounted for the majority of variations in the vegetation C turnover times. Forest origin (planted or natural forest) was also responsible for the variations in vegetation C turnover times, while forest type and soil properties were not the dominant controlling factors. Our study highlights the different dominant controlling factors in soil and vegetation C turnover times and different mechanisms underlying above‐ and below‐ground C turnover. These findings are essential to better understand (and reduce uncertainty) in predictive models of coupled C–climate system. A plain language summary is available for this article. Plain Language Summary

The knowledge of tree species distribution at a national scale provides benefits for forest management practices and decision making for site-adapted tree species selection. An accurate assignment of tree species in relation to their location allows conclusions about potential resilience or vulnerability to biotic and abiotic factors. Identifying areas at risk helps the long-term strategy of forest conversion towards a natural, diverse, and climate-resilient forest. In the framework of the national forest inventory (NFI) in Germany, data on forest tree species are collected in sample plots, but there is a lack of a full coverage map of the tree species distribution. The NFI data were used to train and test a machine-learning approach that classifies a dense Sentinel-2 time series with the result of a dominant tree species map of German forests with seven main tree species classes. The test of the model’s accuracy for the forest type classification showed a weighted average F1-score for deciduous tree species (Beech, Oak, Larch, and Other Broadleaf) between 0.77 and 0.91 and for non-deciduous tree species (Spruce, Pine, and Douglas fir) between 0.85 and 0.94. Two additional plausibility checks with independent forest stand inventories and statistics from the NFI show conclusive agreement. The results are provided to the public via a web-based interactive map, in order to initiate a broad discussion about the potential and limitations of satellite-supported forest management.

Depending on the strength of the relationship between biodiversity and aboveground biomass (AGB), diversity loss could lead to varied declines in carbon storage, compromising the role of forests as carbon sink. This study assesses different factors affecting the diversity–AGB relationship, including small trees (diameter < 7.5 cm) and considering different diversity metrics (Hill numbers), plot sizes (80, 400 and 1000 m2) and successional age categories (8–22, 23–30 and > 60 years). The study compares these relationships across three types of tropical dry forests: deciduous, semi-deciduous, and semi-evergreen. Results reveal the highest deviance values in plots with large trees in the 400 m2 size (d2 = 40.4), decreasing when small trees were included (d2 = 25.8). Higher deviance values show the major contribution of large trees to diversity and AGB of 400 m2 plots, while lower deviance values indicate the high contribution of small trees to diversity but limited contribution to AGB. When analyzing only large trees, deviance decreased with the order of Hill numbers. However, incorporating small trees increased deviance for higher Hill numbers. This is because abundance of small and large trees together has a greater influence on AGB. The diversity–AGB relationship was more prevalent and stronger in the semideciduous forest, which had marked orographic and successional age variation. The strongest diversity–AGB effect occurred in early successional ages, weakening in older stages. Our results show that accuracy in estimating the diversity–AGB relationship varies with plant size, diversity parameters, plot size and forest type.

Forest structure and composition regulate a range of ecosystem services, including biodiversity, water and nutrient cycling, and wood volume for resource extraction. Forest type is an important metric measured in the US Forest Service Forest Inventory and Analysis (FIA) program, the national forest inventory of the USA. Forest type information can be used to quantify carbon and other forest resources within specific domains to support ecological analysis and forest management decisions, such as managing for disease and pests. In this study, we developed a methodology that uses a combination of airborne hyperspectral and lidar data to map FIA-defined forest type between sparsely sampled FIA plot data collected in interior Alaska. To determine the best classification algorithm and remote sensing data for this task, five classification algorithms were tested with six different combinations of raw hyperspectral data, hyperspectral vegetation indices, and lidar-derived canopy and topography metrics. Models were trained using forest type information from 632 FIA subplots collected in interior Alaska. Of the thirty model and input combinations tested, the random forest classification algorithm with hyperspectral vegetation indices and lidar-derived topography and canopy height metrics had the highest accuracy (78% overall accuracy). This study supports random forest as a powerful classifier for natural resource data. It also demonstrates the benefits from combining both structural (lidar) and spectral (imagery) data for forest type classification.

Studying carbon and nitrogen stocks in different types of larch forest ecosystems is of great significance for assessing the carbon sink capacity and nitrogen level in larch forests. To evaluate the effects of the differences of forest type on the carbon and nitrogen stock capacity of the larch forest ecosystem, we selected three typical types of larch forest ecosystems in the northern part of Daxing’an Mountains, which were the Rhododendron simsii-Larix gmelinii forest (RL), Ledum palustre-Larix gmelinii forest (LL) and Sphagnum-Bryum-Ledum palustre-Larix gmelinii forest (SLL), to determine the carbon and nitrogen stocks in the vegetation (trees and understories), litter and soil. Results showed that there were significant differences in carbon and nitrogen stocks among the three types of larch forest ecosystems, showing a sequence of SLL (288.01 Mg·ha−1 and 25.19 Mg·ha−1) > LL (176.52 Mg·ha−1 and 14.85 Mg·ha−1) > RL (153.93 Mg·ha−1 and 10.00 Mg·ha−1) (P < 0.05). The largest proportions of carbon and nitrogen stocks were found in soils, accounting for 83.20%, 72.89% and 64.61% of carbon stocks and 98.61%, 97.58% and 96.00% of nitrogen stocks in the SLL, LL and RL, respectively. Also, it was found that significant differences among the three types of larch forest ecosystems in terms of soil carbon and nitrogen stocks (SLL > LL > RL) (P < 0.05) were the primary reasons for the differences in the ecosystem carbon and nitrogen stocks. More than 79% of soil carbon and 51% of soil nitrogen at a depth of 0–100 cm were stored in the upper 50 cm of the soil pool. In the vegetation layer, due to the similar tree biomass carbon and nitrogen stocks, there were no significant differences in carbon and nitrogen stocks among the three types of larch forest ecosystems. The litter carbon stock in the SLL was significantly higher than that in the LL and RL (P < 0.05), but no significant differences in nitrogen stock were found among them (P > 0.05). These findings suggest that different forest types with the same tree layer and different understory vegetation can greatly affect the carbon and nitrogen stock capacity of the forest ecosystem. This indicates that understory vegetation may have significant effects on the carbon and nitrogen stocks in soil and litter, which highlights the need to consider the effects of understory in future research into the carbon and nitrogen stock capacity of forest ecosystems.

Soil microarthropods are essential for nutrient cycling in forest ecosystems as they are integral components of decomposer food webs. They channel carbon and nutrients from leaf litter and roots to higher trophic levels; however, knowledge on the relative importance of different channels and on their variation with forest type is lacking. Although the importance of root‐derived inputs for sustaining soil food webs is increasingly recognized, the pathways by which they are channeled to higher trophic levels are little understood. For the channeling, ectomycorrhizal fungi may play a significant role, but until now methods allowing to separate the contribution of ectomycorrhizal and saprotrophic fungi to the nutrition of soil animal communities are lacking. Using dual analysis of 15N and 13C in amino acids (AAs), we investigated trophic positions and basal resources of two major groups of soil microarthropods, Collembola and Oribatida, in beech and spruce forests in Germany. By applying a 13C fingerprinting approach and Bayesian mixing models, we separated in a first step the relative contribution of bacteria, fungi, and plants to the nutrition of soil microarthropods. As fungi were identified as the major food source, in a second step we attempted to separate the contribution of ectomycorrhizal vs. saprotrophic fungi. For the first time, we provide direct evidence that soil microarthropods mainly rely on saprotrophic fungi, whereas ectomycorrhizal fungi are consumed by only few species. While trophic niches of Collembola and Oribatida species generally varied little between beech and spruce forests, plant detritus as basal resource of soil microarthropods was somewhat more important in beech forests, whereas in spruce forests microbial resources dominated. Overall, the dual analysis of carbon and nitrogen in AAs provided insight into food web structure of soil microarthropods in unprecedented detail, and for the first time allowed to estimate the relative importance of mycorrhizal and saprotrophic fungi for soil food web nutrition, a long‐standing riddle in soil food web ecology. The technique provides the perspective for a comprehensive understanding of the trophic structure and energy channeling in soil food webs.

1. Variable-retention harvest is widely recognized as an alternative to more intensive methods such as clear-cutting. However, present information is inadequate to judge the impact of variable retention on biodiversity of indigenous forest organisms intolerant of canopy removal, such as forest-inhabiting bryophytes. 2. We examined how bryophyte species cover, richness, diversity and composition change with time in response to a broad range of dispersed retention harvest treatments (2% [clear-cut], 10%, 20%, 50%, 75% retention of original basal area) contrasted with uncut controls [100% retention]) in broadleaf deciduous, mixedwood and conifer-dominated boreal forests in North West Alberta, Canada. Bryophytes were studied in 432 permanent sample plots within 72 compartments before harvest and at 3, 6 and 11 years after harvest. 3. Clear-cut and lower (10% and 20%) retention levels resulted in lower cover and richness of bryophytes than in unharvested control compartments in mixed and conifer-dominated forests, but less so in deciduous-dominated forests, which generally supported low cover and richness. Species composition in each forest type varied along the gradient of harvesting intensity; clear-cuts and lower levels of retention supported similar composition, as did control plots and those representing higher retention levels. Over time, the retention harvest treatments became more similar to uncut controls. 4. Synthesis and applications. Variable-retention harvests can better maintain bryophyte biodiversity in managed boreal mixedwood forests, as compared to clearcuts. We found the efficacy of retention harvest scaled with harvest intensity. Higher levels of retention better moderated the negative impacts of harvesting on bryophyte assemblages across all forest types. Our results suggest, however, that even 10% retention will facilitate faster post-harvest recovery of bryophytes, as compared to clear-cutting.

Rubber ( Hevea brasiliensis (Wild. Ex Adr. De Juss.) Muell. Arg.) is emerging as a fast-expanding plantation crop in India and Southeast Asia. Traditionally, aboveground biomass (AGB) is estimated from forest type or crown density stratification by the Forest Survey of India (FSI) and does not explicitly account for standing age. The present study estimates the AGB and carbon (C) stock of natural rubber (NR) plantations in Tripura, India, which were estimated to cover 93 thousand hectares (kha) in 2021 using remote sensing. A multi-year satellite data-based rubber plantation age-class map was used with measured AGB to generate age-based rubber AGB and C-stock maps with 5-year interval age classes. The total carbon stored for all age group rubber plantations was found to be 2.8 Tg. State-level forest cover and type statistics from the Forest Survey of India (FSI) biannual reports, i.e. India State of Forest Report, were used to understand the dynamics of the forest over the past two decades. This study indicates that the expansion of rubber plantations was accompanied by a loss in natural vegetation and a reduction in standing pools. While India is committed to reducing carbon emissions, and NR plantations have the potential to be an important source of C-stocks at the state and national levels, results indicate that this study site has undergone significant changes in natural forest cover and type. The developed approach may be utilized in practical applications for accurate C-stock accounting in other managed forests.

It is critical to acquire the information of forest type at the tree species level due to its strong links with various quantitative and qualitative indicators in forest inventories. The efficiency of deep-learning classification models for high spatial resolution (HSR) remote sensing image has been demonstrated with the ongoing development of artificial intelligence technology. However, due to limited statistical separability and complicated circumstances, completely automatic and highly accurate forest type mapping at the tree species level remains a challenge. To deal with the problem, a novel deep fusion uNet model was developed to improve the performance of forest classification refined at the dominant tree species level by combining the beneficial phenological characteristics of the multi-temporal imagery and the powerful features of the deep uNet model. The proposed model was built on a two-branch deep fusion architecture with the deep Res-uNet model functioning as its backbone. Quantitative assessments of China’s Gaofen-2 (GF-2) HSR satellite data revealed that the suggested model delivered a competitive performance in the Wangyedian forest farm, with an overall classification accuracy (OA) of 93.30% and a Kappa coefficient of 0.9229. The studies also yielded good results in the mapping of plantation species such as the Chinese pine and the Larix principis.